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                        Introduction
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                            <b>1.</b>
                        
                        第一章：数据分析前奏
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                            <b>1.1.</b>
                        
                        第1节：什么是数据分析
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                            <b>1.2.</b>
                        
                        第2节：环境搭建
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                            <b>1.3.</b>
                        
                        第3节：jupyternotebook使用
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                            <b>1.4.</b>
                        
                        第4节：作业
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                            <b>2.</b>
                        
                        第二章：Numpy库
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                            <b>2.1.</b>
                        
                        第1节：numpy库介绍
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                            <b>2.2.</b>
                        
                        第2节：numpy数组基本
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                            <b>2.3.</b>
                        
                        第3节：numpy数组操作
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                            <b>2.4.</b>
                        
                        第4节：numpy索引和切片
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                            <b>2.5.</b>
                        
                        第5节：Numpy索引和切片作业
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                            <b>2.6.</b>
                        
                        第6节：numpy数组操作
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                            <b>2.7.</b>
                        
                        第7节：深拷贝和浅拷贝
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                            <b>2.8.</b>
                        
                        第8节：文件操作
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                            <b>2.9.</b>
                        
                        第9节：数组操作和文件操作作业.md
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                            <b>2.10.</b>
                        
                        第10节：NAN和INF值处理.md
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                            <b>2.11.</b>
                        
                        第11节：random模块.md
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                            <b>2.12.</b>
                        
                        第12节：axis理解.md
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                            <b>2.13.</b>
                        
                        第13节：通用函数.md
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                            <b>2.14.</b>
                        
                        第14节：numpy练习题.md
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                            <b>3.</b>
                        
                        第三章：Pandas库
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                            <b>3.1.</b>
                        
                        第1节：pandas介绍
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                            <b>3.2.</b>
                        
                        第2节：pandas索引操作
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                            <b>3.3.</b>
                        
                        第3节：pandas对齐运算
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                            <b>3.4.</b>
                        
                        第4节：pandas函数应用
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                            <b>3.5.</b>
                        
                        第5节：pandas层级索引
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                            <b>3.6.</b>
                        
                        第6节：pandas统计计算和描述
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                            <b>3.7.</b>
                        
                        第7节：文件操作
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                            <b>3.8.</b>
                        
                        第8节：数据清洗
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                            <b>3.9.</b>
                        
                        第9节：聚合和分组
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                            <b>3.10.</b>
                        
                        第10节：时间序列
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                            <b>4.</b>
                        
                        第四章：Matploblib库
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                            <b>4.1.</b>
                        
                        第1节：常用图
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                            <b>4.2.</b>
                        
                        第2节：基本使用
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                            <b>4.3.</b>
                        
                        第3节：条形图
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                            <b>4.4.</b>
                        
                        第4节：直方图
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                            <b>4.5.</b>
                        
                        第5节：散点图
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                            <b>4.6.</b>
                        
                        第6节：饼图
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                            <b>4.7.</b>
                        
                        第7节：箱线图
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                            <b>4.8.</b>
                        
                        第8节：雷达图
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                            <b>4.9.</b>
                        
                        第9节：matplotlib绘图分析
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                            <b>4.10.</b>
                        
                        第10节：多图布局
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                            <b>4.11.</b>
                        
                        第11节：matplotlib配置
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                            <b>4.12.</b>
                        
                        第12节：matplotlib作业
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                            <b>5.</b>
                        
                        第五章：Seaborn库
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                            <b>5.1.</b>
                        
                        第1节：关系绘图
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                            <b>5.2.</b>
                        
                        第2节：分类绘图
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                            <b>5.3.</b>
                        
                        第3节：分布绘图
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                            <b>5.4.</b>
                        
                        第4节：线性关系绘图
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                            <b>5.5.</b>
                        
                        第5节：FacetGrid结构图
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                            <b>5.6.</b>
                        
                        第6节：样式设置
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                            <b>5.7.</b>
                        
                        第7节：调色盘设置
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                            <b>5.8.</b>
                        
                        第8节：seaborn作业
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                            <b>6.</b>
                        
                        第六章：统计分析强化
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                            <b>6.1.</b>
                        
                        第1节：常用专业数学术语
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                            <b>6.2.</b>
                        
                        第2节：平均数
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                            <b>6.3.</b>
                        
                        第3节：方差和标准差
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                            <b>6.4.</b>
                        
                        第4节：正态分布
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                            <b>6.5.</b>
                        
                        第5节：对比分析
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                            <b>6.6.</b>
                        
                        第6节：分布分析
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                            <b>6.7.</b>
                        
                        第7节：交叉分析
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                            <b>6.8.</b>
                        
                        第8节：统计分析
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                            <b>6.9.</b>
                        
                        第9节：帕累托分析
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                            <b>6.10.</b>
                        
                        第10节：矩阵关联分析
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                            <b>6.11.</b>
                        
                        第11节：综合性分析
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                            <b>7.</b>
                        
                        第七章：数据分析实战
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                            <b>7.1.</b>
                        
                        第1节：App数据分析实战
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                            <b>7.2.</b>
                        
                        第2节：心脏病患者数据分析
                    </a>
            
            
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                            <b>7.3.</b>
                        
                        第3节：StackoverFlow数据分析
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                            <b>7.4.</b>
                        
                        第4节：二手房数据分析
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                            <b>7.5.</b>
                        
                        第5节：吃鸡数据分析
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                            <b>7.6.</b>
                        
                        第6节：黑色星期五数据分析
                    </a>
            
            
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                            <b>8.</b>
                        
                        第八章：补充
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                            <b>8.1.</b>
                        
                        第1节：用Excel做数据分析
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                            <b>8.2.</b>
                        
                        第2节：echarts和pyecharts库
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                            <b>8.3.</b>
                        
                        第3节：bokeh库
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                        <h1 id="&#x591A;&#x56FE;&#x5E03;&#x5C40;">&#x591A;&#x56FE;&#x5E03;&#x5C40;</h1>
<h2 id="&#x89E3;&#x51B3;&#x5143;&#x7D20;&#x91CD;&#x53E0;&#x7684;&#x95EE;&#x9898;&#xFF1A;">&#x89E3;&#x51B3;&#x5143;&#x7D20;&#x91CD;&#x53E0;&#x7684;&#x95EE;&#x9898;&#xFF1A;</h2>
<p>&#x5728;&#x4E00;&#x4E2A;<code>Figure</code>&#x4E0A;&#x9762;&#xFF0C;&#x53EF;&#x80FD;&#x5B58;&#x5728;&#x591A;&#x4E2A;<code>Axes</code>&#x5BF9;&#x8C61;&#xFF0C;&#x5982;&#x679C;<code>Figure</code>&#x6BD4;&#x8F83;&#x5C0F;&#xFF0C;&#x90A3;&#x4E48;&#x6709;&#x53EF;&#x80FD;&#x4F1A;&#x9020;&#x6210;&#x4E00;&#x4E9B;&#x56FE;&#x5F62;&#x5143;&#x7D20;&#x91CD;&#x53E0;&#xFF0C;&#x8FD9;&#x65F6;&#x5019;&#x6211;&#x4EEC;&#x5C31;&#x53EF;&#x4EE5;&#x901A;&#x8FC7;<code>fig.tight_layout</code>&#x6216;&#x8005;&#x662F;<code>fig.subplots_adjust</code>&#x65B9;&#x6CD5;&#x6765;&#x5E2E;&#x6211;&#x4EEC;&#x8C03;&#x6574;&#x3002;&#x5047;&#x5982;&#x73B0;&#x5728;&#x6CA1;&#x6709;&#x7ECF;&#x8FC7;&#x8C03;&#x6574;&#xFF0C;&#x90A3;&#x4E48;&#x4EE5;&#x4E0B;&#x4EE3;&#x7801;&#x7684;&#x6548;&#x679C;&#x56FE;&#x5982;&#x4E0B;&#xFF1A;</p>
<pre><code class="lang-python"><span class="hljs-keyword">import</span> matplotlib.pyplot <span class="hljs-keyword">as</span> plt
<span class="hljs-keyword">import</span> numpy <span class="hljs-keyword">as</span> np

<span class="hljs-function"><span class="hljs-keyword">def</span> <span class="hljs-title">example_plot</span><span class="hljs-params">(ax, fontsize=<span class="hljs-number">12</span>)</span>:</span>
    ax.plot([<span class="hljs-number">1</span>, <span class="hljs-number">2</span>])
    ax.set_xlabel(<span class="hljs-string">&apos;x-label&apos;</span>, fontsize=fontsize)
    ax.set_ylabel(<span class="hljs-string">&apos;y-label&apos;</span>, fontsize=fontsize)
    ax.set_title(<span class="hljs-string">&apos;Title&apos;</span>, fontsize=fontsize)

fig,axes = plt.subplots(<span class="hljs-number">2</span>,<span class="hljs-number">2</span>)
fig.set_facecolor(<span class="hljs-string">&quot;y&quot;</span>)
example_plot(axes[<span class="hljs-number">0</span>,<span class="hljs-number">0</span>])
example_plot(axes[<span class="hljs-number">0</span>,<span class="hljs-number">1</span>])
example_plot(axes[<span class="hljs-number">1</span>,<span class="hljs-number">0</span>])
example_plot(axes[<span class="hljs-number">1</span>,<span class="hljs-number">1</span>])
</code></pre>
<p>&#x6548;&#x679C;&#x56FE;&#x5982;&#x4E0B;&#xFF1A;<br><img src="../assets/chapter04/&#x6CA1;&#x6709;tight_layout&#x6548;&#x679C;&#x56FE;.png" alt=""></p>
<p>&#x4E3A;&#x4E86;&#x907F;&#x514D;&#x591A;&#x4E2A;&#x56FE;&#x91CD;&#x53E0;&#xFF0C;&#x53EF;&#x4EE5;&#x4F7F;&#x7528;<code>plt.tight_layout</code>&#x6765;&#x5B9E;&#x73B0;&#xFF1A;</p>
<pre><code class="lang-python"><span class="hljs-comment"># &#x4E4B;&#x524D;&#x7684;&#x4EE3;&#x7801;...</span>
plt.tight_layout()
</code></pre>
<p>&#x6548;&#x679C;&#x56FE;&#x5982;&#x4E0B;&#xFF1A;<br><img src="../assets/chapter04/&#x6709;tight_layout&#x6548;&#x679C;&#x56FE;.png" alt=""></p>
<p>&#x5176;&#x4E2D;<code>tight_layout</code>&#x8FD8;&#x6709;&#x4E24;&#x4E2A;&#x53C2;&#x6570;&#x53EF;&#x4EE5;&#x4F7F;&#x7528;&#xFF0C;&#x5206;&#x522B;&#x662F;<code>w_pad</code>&#x548C;<code>h_pad</code>&#xFF0C;&#x8FD9;&#x4E24;&#x4E2A;&#x53C2;&#x6570;&#x5206;&#x522B;&#x8868;&#x793A;&#x7684;&#x610F;&#x601D;&#x662F;&#x5728;&#x6C34;&#x5E73;&#x65B9;&#x5411;&#x7684;&#x56FE;&#x4E4B;&#x95F4;&#x7684;&#x95F4;&#x8DDD;&#xFF0C;&#x4EE5;&#x53CA;&#x5728;&#x5782;&#x76F4;&#x65B9;&#x5411;&#x8FD9;&#x4E9B;&#x56FE;&#x7684;&#x95F4;&#x8DDD;&#x3002;</p>
<p>&#x53E6;&#x5916;&#x4E5F;&#x53EF;&#x4EE5;&#x901A;&#x8FC7;<code>fig.subplots_adjust(left=None,bottom=None,right=None,top=None,wspace=None,hspace=None)</code>&#x6765;&#x5B9E;&#x73B0;&#xFF0C;&#x6548;&#x679C;&#x5982;&#x4E0B;&#xFF1A;</p>
<pre><code class="lang-python"><span class="hljs-comment"># &#x4E4B;&#x524D;&#x7684;&#x4EE3;&#x7801;...</span>
fig.subplots_adjust(<span class="hljs-number">0</span>,<span class="hljs-number">0</span>,<span class="hljs-number">1</span>,<span class="hljs-number">1</span>,hspace=<span class="hljs-number">0.5</span>,wspace=<span class="hljs-number">0.5</span>)
</code></pre>
<p>&#x6548;&#x679C;&#x56FE;&#x5982;&#x4E0B;&#xFF1A;
<img src="../assets/chapter04/&#x81EA;&#x5B9A;&#x4E49;&#x5E03;&#x5C40;4.png" alt=""></p>
<h2 id="&#x81EA;&#x5B9A;&#x4E49;&#x5E03;&#x5C40;&#x65B9;&#x5F0F;&#xFF1A;">&#x81EA;&#x5B9A;&#x4E49;&#x5E03;&#x5C40;&#x65B9;&#x5F0F;&#xFF1A;</h2>
<p>&#x5982;&#x679C;&#x5E03;&#x5C40;&#x4E0D;&#x662F;&#x56FA;&#x5B9A;&#x7684;&#x51E0;&#x5BAB;&#x683C;&#x7684;&#x65B9;&#x5F0F;&#xFF0C;&#x800C;&#x662F;&#x67D0;&#x4E2A;&#x56FE;&#x5360;&#x636E;&#x4E86;&#x591A;&#x884C;&#x6216;&#x8005;&#x591A;&#x5217;&#xFF0C;&#x90A3;&#x4E48;&#x5C31;&#x9700;&#x8981;&#x91C7;&#x7528;&#x4E00;&#x4E9B;&#x624B;&#x6BB5;&#x6765;&#x5B9E;&#x73B0;&#x3002;&#x5982;&#x679C;&#x4E0D;&#x662F;&#x5F88;&#x590D;&#x6742;&#xFF0C;&#x90A3;&#x4E48;&#x76F4;&#x63A5;&#x53EF;&#x4EE5;&#x901A;&#x8FC7;<code>subplot</code>&#x7B49;&#x65B9;&#x6CD5;&#x6765;&#x5B9E;&#x73B0;&#x3002;&#x793A;&#x4F8B;&#x4EE3;&#x7801;&#x5982;&#x4E0B;&#xFF1A;</p>
<pre><code class="lang-python">ax1 = plt.subplot(<span class="hljs-number">221</span>)
ax2 = plt.subplot(<span class="hljs-number">223</span>)
ax3 = plt.subplot(<span class="hljs-number">122</span>)
</code></pre>
<p>&#x6548;&#x679C;&#x56FE;&#x5982;&#x4E0B;&#xFF1A;<br><img src="../assets/chapter04/&#x81EA;&#x5B9A;&#x4E49;&#x5E03;&#x5C40;1.png" alt=""></p>
<p>&#x4F46;&#x662F;&#x5982;&#x679C;&#x5B9E;&#x73B0;&#x7684;&#x5E03;&#x5C40;&#x6BD4;&#x8F83;&#x590D;&#x6742;&#xFF0C;&#x90A3;&#x4E48;&#x5C31;&#x9700;&#x8981;&#x91C7;&#x7528;<code>GridSpec</code>&#x5BF9;&#x8C61;&#x6765;&#x5B9E;&#x73B0;&#x3002;&#x793A;&#x4F8B;&#x4EE3;&#x7801;&#x5982;&#x4E0B;&#xFF1A;</p>
<pre><code class="lang-python">fig = plt.figure()
<span class="hljs-comment"># &#x521B;&#x5EFA;3&#x884C;3&#x5217;&#x7684;GridSpec&#x5BF9;&#x8C61;</span>
gs = fig.add_gridspec(<span class="hljs-number">3</span>,<span class="hljs-number">3</span>)
ax1 = fig.add_subplot(gs[<span class="hljs-number">0</span>,<span class="hljs-number">0</span>:<span class="hljs-number">3</span>])
ax1.set_title(<span class="hljs-string">&quot;[0,0:3]&quot;</span>)
ax2 = fig.add_subplot(gs[<span class="hljs-number">1</span>,<span class="hljs-number">0</span>:<span class="hljs-number">2</span>])
ax2.set_title(<span class="hljs-string">&quot;[1,0:2]&quot;</span>)
ax3 = fig.add_subplot(gs[<span class="hljs-number">1</span>:<span class="hljs-number">3</span>,<span class="hljs-number">2</span>])
ax3.set_title(<span class="hljs-string">&quot;[1:3,2]&quot;</span>)
ax4 = fig.add_subplot(gs[<span class="hljs-number">2</span>,<span class="hljs-number">0</span>])
ax4.set_title(<span class="hljs-string">&quot;[2,0]&quot;</span>)
ax5 = fig.add_subplot(gs[<span class="hljs-number">2</span>,<span class="hljs-number">1</span>])
ax5.set_title(<span class="hljs-string">&quot;[2,1]&quot;</span>)
plt.tight_layout()
</code></pre>
<p>&#x6548;&#x679C;&#x56FE;&#x5982;&#x4E0B;&#xFF1A;<br><img src="../assets/chapter04/&#x81EA;&#x5B9A;&#x4E49;&#x5E03;&#x5C40;2.png" alt=""></p>
<p>&#x4E5F;&#x53EF;&#x4EE5;&#x8BBE;&#x7F6E;&#x5BBD;&#x9AD8;&#x6BD4;&#x4F8B;&#x3002;&#x793A;&#x4F8B;&#x4EE3;&#x7801;&#x5982;&#x4E0B;&#xFF1A;</p>
<pre><code class="lang-python"><span class="hljs-comment"># &#x8BBE;&#x7F6E;&#x5BBD;&#x5EA6;&#x6BD4;&#x4F8B;&#x4E3A;1:2:1</span>
widths = (<span class="hljs-number">1</span>,<span class="hljs-number">2</span>,<span class="hljs-number">1</span>)
<span class="hljs-comment"># &#x8BBE;&#x7F6E;&#x9AD8;&#x5EA6;&#x6BD4;&#x4F8B;&#x4E3A;2:2:1</span>
heights = (<span class="hljs-number">2</span>,<span class="hljs-number">2</span>,<span class="hljs-number">1</span>)
fig = plt.figure()
<span class="hljs-comment"># &#x521B;&#x5EFA;GridSpec&#x5BF9;&#x8C61;&#x7684;&#x65F6;&#x5019;&#x6307;&#x5B9A;&#x5BBD;&#x9AD8;&#x7684;&#x6BD4;</span>
gs = fig.add_gridspec(<span class="hljs-number">3</span>,<span class="hljs-number">3</span>,width_ratios=widths,height_ratios=heights)
<span class="hljs-keyword">for</span> row <span class="hljs-keyword">in</span> range(<span class="hljs-number">0</span>,<span class="hljs-number">3</span>):
    <span class="hljs-keyword">for</span> col <span class="hljs-keyword">in</span> range(<span class="hljs-number">0</span>,<span class="hljs-number">3</span>):
        fig.add_subplot(gs[row,col])
plt.tight_layout()
</code></pre>
<p>&#x6548;&#x679C;&#x56FE;&#x5982;&#x4E0B;&#xFF1A;<br><img src="../assets/chapter04/&#x81EA;&#x5B9A;&#x4E49;&#x5E03;&#x5C40;3.png" alt=""></p>
<h2 id="&#x624B;&#x52A8;&#x8BBE;&#x7F6E;&#x4F4D;&#x7F6E;&#xFF1A;">&#x624B;&#x52A8;&#x8BBE;&#x7F6E;&#x4F4D;&#x7F6E;&#xFF1A;</h2>
<p>&#x901A;&#x8FC7;<code>fig.add_axes</code>&#x7684;&#x65B9;&#x5F0F;&#x6DFB;&#x52A0;<code>Axes</code>&#x5BF9;&#x8C61;&#xFF0C;&#x53EF;&#x4EE5;&#x76F4;&#x63A5;&#x6307;&#x5B9A;&#x4F4D;&#x7F6E;&#x3002;&#x4E5F;&#x53EF;&#x4EE5;&#x5728;&#x6DFB;&#x52A0;&#x5B8C;&#x6210;&#x540E;&#xFF0C;&#x901A;&#x8FC7;<code>axes.set_position</code>&#x7684;&#x65B9;&#x5F0F;&#x8BBE;&#x7F6E;&#x4F4D;&#x7F6E;&#x3002;&#x793A;&#x4F8B;&#x4EE3;&#x7801;&#x5982;&#x4E0B;&#xFF1A;</p>
<pre><code class="lang-python"><span class="hljs-comment"># add_axes&#x7684;&#x65B9;&#x5F0F;</span>
fig = plt.figure()
fig.add_subplot(<span class="hljs-number">111</span>)
fig.add_axes([<span class="hljs-number">0.2</span>,<span class="hljs-number">0.2</span>,<span class="hljs-number">0.4</span>,<span class="hljs-number">0.4</span>])

<span class="hljs-comment"># &#x8BBE;&#x7F6E;position&#x7684;&#x65B9;&#x5F0F;</span>
fig,axes = plt.subplots(<span class="hljs-number">1</span>,<span class="hljs-number">2</span>)
axes[<span class="hljs-number">1</span>].set_position([<span class="hljs-number">0.2</span>,<span class="hljs-number">0.2</span>,<span class="hljs-number">0.4</span>,<span class="hljs-number">0.4</span>])
</code></pre>
<h2 id="&#x6563;&#x70B9;&#x56FE;&#x548C;&#x76F4;&#x65B9;&#x56FE;&#x5408;&#x5E76;&#x5B9E;&#x6218;&#xFF1A;">&#x6563;&#x70B9;&#x56FE;&#x548C;&#x76F4;&#x65B9;&#x56FE;&#x5408;&#x5E76;&#x5B9E;&#x6218;&#xFF1A;</h2>
<pre><code class="lang-python">fig = plt.figure(figsize=(<span class="hljs-number">8</span>,<span class="hljs-number">8</span>))
widths = (<span class="hljs-number">2</span>,<span class="hljs-number">0.5</span>)
heights = (<span class="hljs-number">0.5</span>,<span class="hljs-number">2</span>)
gs = fig.add_gridspec(<span class="hljs-number">2</span>,<span class="hljs-number">2</span>,width_ratios=widths,height_ratios=heights)
<span class="hljs-comment"># &#x9876;&#x90E8;&#x7684;&#x76F4;&#x65B9;&#x56FE;</span>
ax1 = fig.add_subplot(gs[<span class="hljs-number">0</span>,<span class="hljs-number">0</span>])
ax1.hist(male_athletes[<span class="hljs-string">&apos;Height&apos;</span>],bins=<span class="hljs-number">20</span>)
<span class="hljs-keyword">for</span> tick <span class="hljs-keyword">in</span> ax1.xaxis.get_major_ticks():
    tick.label1On = <span class="hljs-keyword">False</span>

<span class="hljs-comment"># &#x4E2D;&#x95F4;&#x7684;&#x6563;&#x70B9;&#x56FE;</span>
ax2 = fig.add_subplot(gs[<span class="hljs-number">1</span>,<span class="hljs-number">0</span>])
ax2.scatter(<span class="hljs-string">&apos;Height&apos;</span>,<span class="hljs-string">&apos;Weight&apos;</span>,data=male_athletes)

<span class="hljs-comment"># &#x53F3;&#x8FB9;&#x7684;&#x76F4;&#x65B9;&#x56FE;</span>
ax3 = fig.add_subplot(gs[<span class="hljs-number">1</span>,<span class="hljs-number">1</span>])
ax3.hist(male_athletes[<span class="hljs-string">&apos;Weight&apos;</span>],bins=<span class="hljs-number">20</span>,orientation=<span class="hljs-string">&apos;horizontal&apos;</span>)
<span class="hljs-keyword">for</span> tick <span class="hljs-keyword">in</span> ax3.yaxis.get_major_ticks():
    tick.label1On = <span class="hljs-keyword">False</span>
fig.tight_layout(h_pad=<span class="hljs-number">0</span>,w_pad=<span class="hljs-number">0</span>)
</code></pre>
<p>&#x6548;&#x679C;&#x56FE;&#x5982;&#x4E0B;&#xFF1A;
<img src="../assets/chapter04/&#x81EA;&#x5B9A;&#x4E49;&#x5E03;&#x5C40;&#x6848;&#x4F8B;.png" alt=""></p>

                    
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